The proposed adaptation of the multi-SNE approach focuses more in producing a good data visualisation by incorporating the labelling information of the samples, which is constructed as a binary matrix of size \(N \times C\) , where \(C\)  presents the total number of classes of the samples and \(N\)  is the number of samples. The cell \((i,j)\)  of the matrix takes the value 1 if sample \(i\)  belongs to class \(j\)  and zero otherwise. For example, in a multi-omics experiment on cancer patients and controls, the labelling matrix will have two columns, one column for the cancer patients, and another one for the controls. Similarly, in a cancer subtypes study where the samples are patients with different cancer types (for example [1]}), each column of the labelling matrix will correspond to each cancer type.
